The methods and apparatus of the present invention were inspired by the challenges of medical imaging. Medical image data is often time-varying, multi-dimensional, subject to imaging artifacts and sensitive to noise. Several examples will illustrate the variety of challenges and performance requirements presented by different imaging techniques. A first example is perfusion imaging, in which textural patterns are to be detected. A second example is the vector field data produced by functional imaging, which changes with time. Analyzing this time-varying data requires focusing on time periods that exhibit significant response of the physiological system being imaged and discarding the periods of low or non-response. A final example is the tensor field data generated by diffusion tensor imaging, a relatively new class of image data which shows tremendous promise in many clinical and research applications.
The constraints and challenges that arise in medical imaging, as illustrated in the previous examples, extend to data analysis in other fields. For example, in addition to the challenges previously described, environmental image processing must also differentiate many types of objects whose properties can vary dramatically.